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Cyber Engineering Research Laboratory (CERL)

Building a Cyber Research Community

CERL draws upon and integrates Sandia's multidisciplinary research strengths in computer science, mathematics, cognitive science, microelectronics, enterprise security, and other fields. CERL is located in Sandia's Science & Technology Park in Albuquerque, New Mexico, which is adjacent to Sandia's main research campus. CERL is also near many high-tech companies and the Unviersity of New Mexico. CERL's research facilities include dedicated labs for human performance, cyber defense exercises, and advanced visualization.

Human Performance Lab

The Human Performance Laboratory uses a variety of cognitive neuroscience methods, including electroencephalography (EEG), eye tracking, and behavioral measures, to study human cognition. The research conducted in the laboratory focuses on investigating techniques for improving cognitive performance. The findings from this research can be applied in cyber and other domains, such as improving training techniques, assessing learning and memory, and helping people to avoid errors.

RECOIL

RECOIL (Research and Engineering for Cyber Operations and Intelligence Laboratory) is a controlled environment for performing cyber exercises that can be used for human-centered cybersecurity research and training. RECOIL expands upon the successful Tracer FIRE cybersecurity training, a live exercise program that Sandia developed for the Department of Energy. RECOIL exercises are regularly performed with students and cybersecurity professionals from academia, industry, and government agencies. RECOIL facilitates partnerships between CERI researchers and academia.

IDEA Lab

Innovation in data analysis occurs when experts collaborate on real-world important questions. The IDEA laboratory enables CERL researchers, visitors, and customers to sit elbow-to-elbow and interactively explore large, complex data sets. This laboratory facilitates collaborative research for methods to visualize, analyze, and make decisions with large, complex data.